1- Introduction

2- Material and methods

3- Results

4- Discussion

5- Conclusions

References

Abstract

Hydrodynamic fluctuations can trigger sediment suspension concomitantly with internal phosphorus release, while the interactive effect of turbulence mixing and sediment suspension on the regulation of phosphorus dynamics is in need of deep understanding. This study addressed the changes in total phosphorus (TP), phosphate (PO43–P) and suspended sediment (SS) in the overlying water, and measured the profile of dissolved oxygen (DO), Fe(II) and soluble reactive phosphorus (SRP) across the sediment-water interface in the simulated environmental turbulence scenario, For a turbulence intensity (ε) of 3.6 × 10−3 m2/s3, the SRP flux increased hence PO43–P showed a 36.36% increase relative to its initial level. Although ε of 1.3 × 10−2 m2/s3 benefited the delivery of oxygen from the bulk aqueous phase to the upper sediment which can trigger the formation of Fe oxides and hydroxides, the turbulence-induced phosphorus diffusion from the sediment exceeded its inactivation and resulted in a large SRP flux. However, a protion of the released PO43–P can be immobilized through SS adsorption and biotic (likely cyanobacteria) assimilation. Higher turbulence intensities (ε of 3.3 × 10−2 and 7.4 × 10−2 m2/s3) led to an approximately 40-fold increase in TP concentration and a significant increase in sediment suspension, which contributed to the immobilization of a majority of the phosphate through adsorption; thus, the PO43–P concentrations in the overlying water displayed 47.75% and 41.67% decline, respectively. This study also confirmed the sequential phosphorus buffer mechanisms associated with increasing turbulence intensities. With an ε of 3.6 × 10−3 m2/s3, bounding to Fe ion had a significant impact on phosphorus inactivation but with an ε of 7.4 × 10−2 m2/s3, the main immobilization mechanism is switched to phosphorus adsorption from the large quantity of suspended sediment.

Introduction

Phosphorus is essential for the support of phytoplankton growth and is a major driving force for water eutrophication (Søndergaard et al., 2013; Conley et al., 2009). Sediment can act as a sources of pollutants in aquatic environments, and the pollutants accumulated in the sediments can be released into the overlying water, contributing a considerable quantity of phosphorus to the water column (Zhang et al., 2018). For most eutrophic waters, the upper 0e10 cm sediment layer typically has much higher phosphorus levels than that is found in the lower sediment layers; and the most heavily contaminated sediment layer is the top 0e5 cm (Tang et al., 2019), where phosphorus is mobile and can be easily released into the overlying water due to changes in the physicochemical characteristics of the water (Soto-Jimenez et al., 2003; Wu et al., 2001). Thus, even if phosphorus input is effectively reduced, the eutrophication level may remain relatively high, as internal phosphorus loading from sediments may lead to algae blooming and cause a decline in the water quality (Huser and Pilgrim, 2014; Yin et al., 2018; Yin et al., 2016). Therefore, understanding the process of phosphorus release from the sediments is crucial for the management and recovery of eutrophic waters.

1- Introduction

2- Regional geological setting

3- Data and methodology

4- Results of the depositional setting and distribution analysis of the Lower Paleozoic shales

5- Results of the tectonism in the Yangtze Platform and control on lower Paleozoic shale gas generation, accumulation and preservation

6- Discussions

7- Conclusions

References

Abstract

Marine shales of the Lower Cambrian Qiongzhusi and Lower Silurian Longmaxi formations are extensively distributed in the Yangtze Platform. Detailed depositional and tectonic analyses and regional mapping indicated that the organic-rich Qiongzhusi-equivalent shale was deposited in an intra-shelf low-slope environment in a passive margin setting. The Longmaxi-equivalent shale was mainly deposited in a widely-distributed intra-shelf low setting in the Upper and Middle Yangtze, and in a distal foredeep area away from clastic dilution in the Lower Yangtze Platform. The transgressive and early highstand system tracts in both Qiongzhusi and Longmaxi shales produced the best-quality shale reservoirs, with high total organic matter (TOC) content (>2%) and high quartz content (>45%). Tectonics has substantial effects on coeval deposition of organic-rich shale and its hydrocarbon accumulation and production. Later tectonism deformed the shales and disrupted early shale gas accumulation. Recent shale gas resource exploration and geologic analysis suggested that the high rate of shale gas production is due to overpressured marine shale reservoirs with high TOC and high gas content in the tectonically-stable area. Shale gas production occurred at reduced rates in slightly underpressured shale reservoirs with lower gas content in the syncline of the tectonically-transitional area deformed by three tectonic movements. There was no gas production from shales in the tectonically-active area disrupted by at least four tectonic movements and complex faults. The results of this analysis indicate that local tectonically-stable areas in the Middle and Lower Yangtze platforms may have shale gas accumulation and production potential.

Introduction

Advances in hydraulic fracturing, horizontal drilling, geological characterization, and characterization of shale properties have greatly expanded shale gas production in the United States, and have revolutionized the US energy market (Curtis, 2002; Steward, 2007; EIA, 2013). In China, shale gas exploration is only just beginning. In 2013, commercially-viable shale gas was produced from the Lower Silurian Longmaxi marine shale in the Fuling Shale Gas Field in the southeast Sichuan Basin, making China the only country outside North America that has reported commercial production (Jiang, 2014). Resource assessment, exploration activities and preliminary production indicate that the Lower Paleozoic marine shales distributed throughout the Yangtze Platform hold the most shale gas reserves (Zhang et al, 2008; Zou et al., 2010; Zhang et al., 2012; Du et al., 2015; Jiang et al., 2015, 2016). Exploration and analyses indicated promising results were mainly from individual pockets in the Sichuan Basin and adjacent areas in the Upper Yangtze Platform located to the west of Huangling anticline in Hubei Province. There was virtually no evidence for shale gas production in the Lower Yangtze Platform (located to the east and southeast of Tanlu Fault), and results indicated limited production in the Middle Yangtze Platform located between Upper and Lower Yangtze Platforms (Tan et al., 2014; Jiang et al., 2015, 2016).

The existing theoretical and empirical models to describe asphaltene deposition in porous media do not consider the complicated structure of pore network. Permeability reduction due to asphaltene deposition has been mainly attributed to pore volume shrinkage (porosity reduction). However, asphaltene particles can also block pore throats which will lead to severe permeability reduction even when a large fraction of total pore volume still remains intact. Thus, there is a need for permeability models that are explicitly function of pore/hydraulic connectivity. This paper provides a review of the existing models and examines a permeability model that explain permeability impairment due to asphaltene deposition. In this study, we propose a new permeability model based on Critical Path Analysis (CPA) which is a function of average coordination number (average number of available/connected neighbor pores). Furthermore, experimental data in the literature related to limestone, sandstone and carbonate (dolomite) samples are utilized to understand combined effects of surface deposition and interconnectivity loss due to pore blockage on permeability reduction. We observed that surface deposition is the dominant mechanism in the limestone samples studied here owing to large pore throat size compared to the particle size. In the sandstone samples, both the surface deposition and pore throat plugging mechanisms contribute fairly the same in the observed permeability reduction. For the carbonate (dolomite) samples, the pore blockage is the dominant mechanism, which results in rapid sharp decrease of the permeability. It is expected that the outcome of this work improves prediction of the asphaltene deposition in the near wellbore region.

Introduction

The issue of asphaltene deposition has plagued the oil and gas industry for decades since it has been identified and named as “asphaltenes” in 1837 [7]. Due to the huge costs associated with remediation, it is extremely important to understand the issue of asphaltene deposition and the factors affecting it [14]. Crude oil has several fractions, and asphaltenes essentially tend to be its heaviest, polarizable fractions. They are known as the “cholesterol of petroleum” due to their ability to precipitate as solids and subsequently deposit with changing pressure, temperature and oil composition [3]. Asphaltene precipitation is called the process when asphaltenes become a separate phase from the crude oil. They remain suspended in the liquid phase where the quantity and the size of the asphaltenes are relatively small. The precipitated asphaltenes clump together (aggregation) and form larger particles, also called flocs. The asphaltene aggregates are initially suspended in the crude oil. Subsequently, the flocs may attach to and accumulate on various surfaces, a process which is called asphaltene deposition [28]. In both up and downstream operations deposition may cause severe problems. Asphaltenes may precipitate and deposit on surface of pipelines, bottom of distillation column and heat exchangers as well, affecting efficiency and creating added economic costs to remediate [18,10]. Also, during production, asphaltene particles can deposit in reservoir, leading to possible blocking of flow, particularly in the near wellbore region. Asphaltene deposition problems encountered deep down in rock reservoirs are extremely problematic, and very challenging to tackle, as opposed to production tubing deposition problems. Minssieux [22] studied various core samples with different rock characteristics in core-flooding experiments, with regards to porous media. He concluded that porous sample plugging only seemed to occur after enough oil had flown through the sample, and that damage at earlier times was only observed in samples with a lower initial permeability [28]. The mechanisms through which formation damage due to asphaltene deposition can occur are surface deposition, and pore throat plugging. As asphaltene deposits accumulate on the pore surface, the pore surface area decreases leading to porosity reduction.

Our understanding of the spatial patterns of landslides in Africa is limited with available landslide studies typically focusing on only one or a few study areas. Moreover, Africa is clearly underrepresented in terms of available landslide inventories. This study aims to produce a first continent-wide landslide susceptibility map for Africa, calibrated with a well-distributed landslide dataset. We reviewed the literature on landslides in Africa and compiled all available landslide inventories (ca. 10,800 landslides), supplemented by additional landslide mapping using Google Earth imagery in underrepresented regions (ca. 7,250 landslides). This resulted in a dataset of approximately 18,050 landslides. Various environmental variables were investigated for their significance in explaining the observed spatial patterns of landslides. To account for potential mapping biases in the dataset, we used Monte Carlo simulations that selected different subsets of mapped landslides to test the significance of the considered environmental variables. Based on these analyses, we constructed two landslide susceptibility maps for Africa: one for all landslide types and one excluding the known rockfalls. In both maps, topography is by far the most significant variable. We evaluated the performance of the fitted multiple logistic regression models using independent subsets of landslides, selected from the total dataset. Overall, both maps perform very well in predicting intra-continental patterns of landslides in Africa and explain about 80% of the observed variance in landslide occurrence. To further test the robustness and sensitivity to mapping biases, we also modelled landslide susceptibility while excluding regions with arid climates, as landslides in these environments are expected to be better preserved over time and therefore likely relatively overrepresented. Despite this potential bias, the effect on the landslide susceptibility model is limited. Based on the constructed database and our analyses we further discuss potential research gaps for landslide prediction in Africa and at continental scales. For example, analysis of the African countries’ mean landslide susceptibility shows a lack of landslide research in various countries prone to landsliding (e.g.: Guinea, Gabon, Lesotho, Madagascar). Apart from the intrinsic value of this landslide susceptibility map as a natural hazard risk management tool, the map and compiled database are highly promising for other applications. For example, we explored the potential significance of landslides as a geomorphic process by confronting our landslide susceptibility map with an available database of measured catchment sediment yield for 500 rivers in Africa. Overall, a significant positive, but relatively weak relation between landslide susceptibility and sediment yield is observed.

Introduction

Studies on landslide risks and fatalities indicate that landslides are present on all continents and are a global threat to humans, infrastructure and the environment (Dilley, 2005; Guzzetti et al., 2012; Haque et al., 2016; Kjekstad & Highland, 2009; Petley, 2012; Sassa & Canuti, 2009; Stanley & Kirschbaum, 2017). While this is certainly also the case for Africa, this continent remains strongly underrepresented in landslide research (e.g. Gariano & Guzzetti, 2016; Kirschbaum et al., 2015, 2010; Maes et al., 2017; Nadim et al., 2006; Petley, 2012; Reichenbach et al., 2018). Also global landslide susceptibility (LSS) maps rely on very few (or no) data of observed landslides in Africa for their calibration (e.g. Hong et al., 2007; Kirschbaum et al., 2009; Nadim et al. 2006; Stanley & Kirschbaum, 2017). Nonetheless, landslides are one of the deadliest natural disasters in Africa (Guha-Sapir et al., 2017). Moreover, their importance and impact are expected to increase due to climate change, with an increase in total precipitation and increasing frequency and intensity of rainstorm events (Gariano & Guzzetti, 2016). This was for instance shown for the Great Lakes region (Shongwe et al., 2011; Souverijns et al., 2016; Thiery et al., 2016), probably one of the most landslide susceptible regions in Africa (Hong et al., 2007; Stanley & Kirschbaum 2017). In addition, Africa is facing a considerable growth in population, which is projected to triple by the end of the 21st century (Gerland et al., 2014). This will likely affect both the frequency and impact of landslide events. For these reasons, earlier studies have urged to fill the gap on landslide research in Africa (e.g. Gariano & Guzzetti, 2016; Jacobs et al., 2016; Maes et al., 2017).

Shape memory alloys (SMAs) are an exquisite class of active materials with an ability to regain its original shape at high temperatures. There is a wide range of alloys which exhibit the shape memory effect, but only those alloys are commercially attractive which show a substantial amount of strain recovery and generate significant force due to shape change. Among the Cu-based shape memory alloys, Cu-Al-Ni alloys have a higher thermal stability than Cu-Zn-Al alloys [1, 2]. Therefore, Cu-Al-Ni alloys are being developed for high temperature applications due to their potential to be used as sensors and actuators at temperatures around 200 ºC. On the other hand, Cu-Zn-Al alloys have maximum working temperatures of 120 °C, but they sh ow better ductility as compared to Cu-Al-Ni alloys for low temperature applications [2]. Shape Memory Effect (SME) is shown by alloys exhibiting crystallographically reversible thermo-elastic martensitic transformation. At a higher temperature, the same alloys have another unique property called as super-elasticity [3]. Superelasticity is caused due to large non-linear recoverable strain (up to 18%) upon loading and unloading, in which a specimen once deformed by application of force regain its original shape automatically without any application of heat [1]. The Ni-Ti, ferrous alloys and Cu-based alloys are considered as practical materials for applications among many shape memory alloys. Ni-Ti alloys, an equi-atomic compound of Ni and Ti, are the most widely used shape memory alloys. They show excellent shape memory strain up to 8 % and are thermally stable. However, the reactivity of Ti limits their processing in air and hence all melting operations are to be carried out in vacuum. In recent decades, Cu-based shape memory alloys have emerged as a potential material for variety of applications, such as high damping material, sensors and actuators. Cu-Al-Ni shape memory alloys have gained special attention due to their high thermal stability among the other Cubased shape memory alloys. The presence of SME, thermo-elastic martensitic transformation and crystallography in the Cu-Al-Ni alloy was confirmed by Otsuka [4-6]. Some ferrous alloys also exhibit SME under certain conditions [7]. Fe-Mn-Si alloys are the most important iron-based shape memory alloy. However, they can recover shape memory strain less than 4 %.

Geostatistics and Geographic Information Systems (GIS) are basic tools used for the georeferential analysis of spatial information, and investigation of spatial variability at various scales (Chang et al., 1999). Geostatistics introduce new tools to many scientific disciplines, permitting interpretation of data in space, and obtaining information necessary in the decision making process. Geostatistical modelling involves the application of numerical methods for particular features of spatial attributes for the purpose of generating the probability model (Olea, 2009). Geostatistical methods are optimal estimation methods, if two conditions are met, i.e., data show normal distribution, and are stationary. Considerable deviations from normality or stationarity may cause problems with interpretation of results. Due to this, the first stage in the analysis of spatial data should be the preparation of a data histogram (Bohling, 2005). The histogram is the most popular way of presentation of the empirical distribution of a parameter (Fig. 1). It is primarily applied when analysing high amounts of data differing to a low degree. If a given value is a total or average value of many inconsiderable random factors, irrespective of the distribution of each of the factors, its distribution will be approximate to normal. The term of stationarity of a variable means that its values do not change with time, and the mean and variance of a given variable should not differ considerably in space. The assumption of stationarity of variables in a model is necessary in the case of introduction of distributions of typical test statistics applied in testing hypotheses. Results of many studies show that when a model involves non-stationary variables, the asymptotic distributions of test statistics are non-standard. This may lead to inaccurate results of statistical inference. During research on air quality and degree of soil pollution, the variability of natural conditions and anthropogenic activity may cause disturbances in measurements, leading to weak stationarity. This should be considered at the further stages of geostatistical modelling among others in the preparation of a semivariogram and interpolation of spatial data by means of the kriging or cokriging method (Brenning, 2001). The determination of the spatial distribution of regionalised variable Z(x) or its moments such as among others semivariance requires obtaining many realisations of z1(x), z2(x), … zn(x). In research on the natural environment, however, only a limited number of realisations of a phenomenon with location x is often available. Such observed distributions are frequently unique, e.g., distribution of soil pollution. In order to obtain the relevant number of realisations of the analysed regionalised variable, it is assumed that the studied phenomenon in a certain area is repeated in space (Zawadzki, 2011).

Soil erosion is a fundamental problem in Ethiopia with tremendous impact on soil quality, land productivity, water pollution and sedimentation (e.g.,Tamene, Park, Dikau, & Vlek, 2006a; Hurni, 1983). In many areas of the mountainous regions of northern Ethiopia, erosion has caused critical loss of topsoil and rapid siltation of water harvesting reservoirs (Adimassu, Mekonnen, Yirga, & Kessler, 2014; Tamene et al., 2006a; Tilahun, Esser, Vägen, & Haile, 2002). To tackle the on- and off-sites damages due to erosion,adequate information on the rates and determinants of soil loss as well as spatial distribution of major sediment sources are needed. Since there are wide differences in the rates of sediment yield from different landscape units and application of conservation measures to all areas experiencing erosion is uneconomical and undesirable, conservation measures should be targeted to critical areas experiencing high soil loss. Identification of “hotspot” areas of erosion is therefore imperative from economic, management and sustainability point of view. Soil erosion models are commonly used to investigate the physical processes and mechanisms governing erosion rates and identifying high risk areas of soil loss to aid conservation planning (Jetten, Govers, & Hessel, 2003; Mitasova, Batron, Ullah, Hofierka, & Harmon, 2013). Recent advances in the development of Geographic Information System (GIS) have promoted the application of distributed soil erosion and sediment delivery models at catchment scales (Kamaludin et al., 2013; Mitasova et al., 2013; Tanyas, Kolatb, & Süzenc, 2015). Studies show that terrain shape and topographic complexity play dominant role on the spatial variation of hydrological processes at the catchment scale (Desmet & Govers, 1996a; Mitasova, Hofierka, Zloch, & Iverson, 1996; Van Oost et. al., 2000). Model formulation with topography being treated in more detail may thus allow reproduction of the basic patterns of erosion and deposition in complex landscapes (Wilson & Gallant, 2000). Soil erosion models that emphasize terrain can be the best compromise between the availability of input data and the reliability of soil loss estimates (Ferro, Di Stefano, & Minacapilli, 2003). The slope steepness-length component of the Universal Soil Loss Equation can be adjusted to appropriately simulate the impacts of complex terrain and various soil-and land-cover changes on the spatial distribution of soil erosion (Desmet & Govers, 1996; Mitasova et al., 1996; Moore & Burch, 1986; Moore, Turner, Wilson, Jensen, & Band, 1993; Wilson & Gallant, 2000). In this study the RUSLE adjusted for complex terrain (RUSLE3D)(e.g.,Desmet & Govers, 1996a; Mitasova et al., 1996) was applied to assess landscape sensitivity to erosion using a spatially distributed model in a GIS environment in northern Ethiopia. The RUSLE3D was integrated with sediment delivery ratio (SDR) to estimate sediment yield of catchments. The model was applied in three catchments of scale ca. 10–20 km2 where different forms of erosion processes and a mosaic of heterogeneous environmental factors are observed. Model results were compared with sediment deposition in reservoirs and with data acquired from field surveys.

The solar energy incident on the Earth’s surface during an hour is almost equal to the one-year total consumption on Earth. When the solar radiation penetrates the atmosphere, a significant amount of its energy is lost due to the fact that Sun radiation is absorbed by solid particles and droplets in the atmosphere and reflected by water vapour and air molecules. But the solar radiation is also absorbed by dust and another type of pollutants as well as scattered backwards, decreasing the direct solar radiation component and causing an increase of the diffuse component. For this reason, the urban and polluted areas typically receive less of the total solar radiation in reference to the clean air of the countryside or industry-free rural areas (Darwish et al. 2018; Robaa 2004). Among the varied practical applications of renewable energy, photovoltaic modules received much attention in electrical power generation and became widely used because of their low production and low maintenance cost as well as their relatively good efficiency of energy conversion which nowadays reaches 26.3% for a single-junction terrestrial cell made of silicon crystalline cell and 38.8% in the case of a five-junction silicon crystalline cell (Green et al. 2011). However, all the types of performance of photovoltaic modules are influenced by a large number of environmental parameters (air temperature, wind speed, air pollution, the angle of incident irradiation, solar radiation spectrum, ageing, snow, dirt and shadowing) (Hassan et al. 2016; Kazem and Chaichan 2016). One of the parameters that influence the energy conversion in PV modules which can be significant in many regions is accumulated dust (Chaichan et al. 2015). The accumulated dust decreases the conversion efficiency because the dust particles reduce and scatter the intensity of the total radiation incident on the PV modules. It was revealed that the dust accumulation rate depends on several parameters such as airborne particle concentration, weather conditions, particle size distribution or type, density, shape, composition, chemistry, charge and variability in reference to the exposure time (Javed et al. 2017). The dust accumulation rate reported in literature varied 1–50 mg m−2 day−1 in Colorado (Boyle et al. 2015) and 150–300 mg m−2 ·day−1 in Minia, Egypt (Hegazy 2001). In another research work, a dust accumulation rate of 132 mg m−2 ·day−1 was reported in Mesa, Arizona (Boppana et al. 2015). The physicochemical properties of deposited dust (Kaldellis and Kapsali 2011), as well as particle size distribution of dust deposited on the surface (Said 1990), have a significant impact on the degradation of PV module performance. The size of deposited solid particles plays a major role in the scattering and absorption of radiation incident on the solar module and causes degradation of PV module efficiency.

مقاله سال 2017

پوشش های فیبر تهی تقویت شده بافته شده با ویژگی های مکانیکی زیاد و سطح ضد رسوب قابل توجه با ترکیب مواد پلی ( وینیل کلرید PVC) با مواد چند پلی ( اتیلن گلیکول متیل اتر متاکریلات) و مواد چند پلیمری (poly(VC-co-PEGMA از طریق تفکیک فاز غیر محلول NIPS آماده میشوند. مقاومت کششی پوشش های فیبر تهی تقویت شده بافته پی وی سی بیشتر از انواع پی وی سی های گزارش شده پوشش های فیبری است.مقاومت خمیدگی بالا در بین سطح ها دلالت بر سازگاری خوب بین مواد پوشش دهنده و ترفتالات پلی اتیلن PET دارد.با توجه به پدیده تجزیه سطحی ، پوشش سطحی PEGMA افزایش نسبت خمیدگی پلی (VC-co-PEGMA)/PVC منجر به دفع آلبومین های سرم گاوی BSA می شوند.برای مقایسه ویژگیهای رسوب گیری ، پوشش های دارای PWP یکسان با تنظیم ساختار مواد برای حذف تاثیر شرایط هیدرودینامیک بر عملکرد رسوب گیری آماده می شوند.سطح پوشش های ترکیی نشان دهنده مقاومت رسوب گیری قابل توجه در جذب ملکولی در محلول BSA و محلول رسوب فعال می باشند. در هر دو مورد ، جریان به 80 درصد جریان اولیه با استفاده از واکنش آب بازیابی می شود. با در نظر گرفتن ویژگیهای مکانیکی و مقاومت ضد رسوب گیری برای فعال سازی محلول رسوب ، این پوسته های جدید پتانسیل خوبی در آزمایشات کاربردی دارند.

1. مقدمه
جذب پوشش یکی از مهم ترین فناوری های تجزیه است که در آزمایشات آب مثل بیوراکتورهای پوسته ای MBR کاربردهای گسترده ای دارند. چندین ماده پلیمری برای ساخت پوشش ها استفاده می شود که از جمله انها می توان به دی فلورید پلی وینیلیدن ، پلی سولفون ،پلی اکریلونیتریت PAN ، و پلی اترسولفون اشاره کرد.پلی وینیل کلرید PVC یک ماده جایگزین مهم برای آمادگی مواد پوششی است و دلیل آن مقاومت مکانیکی بسیار خوب ،مقاومت زیاد در برابر فرسایش و هزینه پایین است.به هر حال ، مشکلات جدی به علت ماهیت هیدروفوبیکی رسوب گیری می تواند در پوشش های پی وی سی اتفاق بیفتد.

مقاله سال 2016

مقدمه

فرمولاسیون مسئله و روند عددی

نتایج محاسبات میدان جریان

مدل انتشار-همرفتی

نتایج محاسبات برای تمرکز میدان های جریان

پیش بینی کمیتی تاثیرات زیست محیطی بر سناریو های تغییر جوی و کاربری زمین در آبخیز می تواند به عنوان مبنایی عالی برای توسعه الگوهای مدیریت آبخیز خدمت می کنند . کمیت و کیفیت آب از جمله شاخص های کلیدی زیست محیطی می باشند که نسبت به انواع اختلال های بیرونی حساس می باشند . هدف این بررسی این است تا تاثیرات تغییر کاربری و تغییرات جوی بر مقدار و کیفیت آب در مقیاس آبخیز و درک روابط بین اجزای آب شناسی و کیفیت آب در آن مقیاس تحت سناریو های مختلف جوی و استفاده از زمین را ارزیابی نمایند . ما یک رویکرد را برای مدل سازی و بررسی تاثیرات استفاده از زمین و سناریو های تغییر جوی در چرخه های آب و ماده مغذی و آب شناسی آبخیز و مدل ماده مغذی را برای آبخیز رودخانه نشیو در هوکایدو در شمال ژاپن توسعه داده ایم . تغییر کاربری زمین پیش بینی شده تحت شرایط جوی و خط مبناء باعث کاهش بارهای رسوب ، نیتروژن و فسفر کلی از آبخیز به رودخانه گردید . این وضعیت را به دریافت ماده مغذی بالاتر توسط غلات و ماده مغذی کمتر معدنی سازی توسط میکروب ها ، نشت ماده مغذی کاهش یافته از خاک و راندمان های آب کمتر در مزرعه نسبت می دهند . تغییرات جوی ( رسوب و دما ) برنامه ریزی شده بودند تا تاثیر بیشتر بر افزایش یافتن جریان آب سطحب ، جریان جانبی ، تخلیه آب زیرزمینی و بازده آب نسبت به تغییر کاربری زمین داشته باشند . رواناب سطحی بویژه در ماه های اوریل و می کاهش یافته بود و در ماه های مارس و دسامبر با زیاد شدن دما کاهش یافته بود .

مقاله سال 2016

مدل های عددی تکامل چشم انداز در ابتدا برای بررسی ژئوموفولوژی و هیدرولوژی آبخیز طبییعی گسترش یافته و ابزار رایجی برای بررسی رفتار ژئومورفیک در مقیاس های زمانی و مکانی شدند. همه این مدل ها از یک مدل ارتفاع دیجیتال (DEM) به عنوان نمایشی از سطح چشم انداز استفاده کرده و یک مسئله مهم، کیفیت و وضوح این سطح است. تمرکز ما در اینجا بر چگونگی تولید نتایج مدل جایگزین توسط نوسانات یا زبری سطح DEM می باشد. این تحقیق با تغییر تصادفی ارتفاعات سطح DEM و بررسی اثر آن بر ژئومورفولوژی و نرخ های انتقال رسوب برای یک طراحی بازسازی مطرح شده برای چشم انداز پس از استخراج معدن با استفاده از فهم های چشم انداز متعدد با افزایش اندازه تغییرات تصادفی انجام می شود. در این تحقیق نشان می دهیم که مقدار افزایشی تغییرپذیری تصادفی سطح، تاثیر قابل ملاحظه ای بر انتقال رسوب در مقیاس های زمانی هزار ساله ندارد. با این حال ، تغییر پذیری تصادفی سطحی الگوی زمانی یا تحویل خروجی رسوب را تا اندازه زیادی تغییر می دهد. یک نتیجه قابل توجه این بود که همه شبیه سازی ها در انتهای 10000 سال دوره مدل سازی به طور ژئومورفولوژی مشابه بوده و یک همپایی ژئومورفولوژی را نشان می دهند. با این حال ، الگوهای فرسایش و ته نشست برای شبیه سازی های مکرر با دنباله متفاوتی از آشفتگی های تصادفی متفاوت بودند. موقعیت های جایگزین آشفتگی های تصادفی تاثیری قوی بر الگوهای محلی تکامل و فرسایش دامنه و الگو و رفتار ته نشست دارند. نتایج، بازخوردهای پیچیده ای را نشان می دهند که حتی در یک سیستم مدلسازی شده ساده رخ می دهند.